Diabetic retinopathy is one of the leading causes of avoidable blindness, which can be detected with fundus examination. A traditional 45-degree non-mydriatic fundus camera often misses the peripheral areas of the retina. Our research project is to develop an artificial intelligence-based deep learning algorithm for assessing the severity and peripheral lesions of diabetic retinopathy in a central 45-degree fundus image using the ground truth identified from an ultra-wide field (UWF) colour imaging system.

This study will be helpful in identifying the early signs of diabetic retinopathy and peripheral lesions that predict diabetic retinopathy progression. Thus, providing the development of treatment and prevention of the disease cost-effectively.

Ms Khan and Dr Roy will collaborate with leading researchers with expertise aligned with the research proposal, including Dr Rajiv Raman, a renowned ophthalmologist,  Vitreoretinal services, Sankara Nethralaya, Chennai, India and Dr Sundaresan Raman, Department of Computer Science and Information Systems, Birla Institute of Technology and Science (BITS), Pilani, India.

We sincerely thank the Elizabeth O’Beirne and Robert and Emmy Mather Trust for their support.